In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

Q: The grammar induction method works on the premise of curriculum learning BIBREF7 , where the parser first learns to parse simple sentences, then proceeds to learn more complex ones. The induction method is iterative, semi-automatic and based on frequent patterns. A context-free grammar (CFG) is induced from the text, which is represented by several layers of semantic annotations. 
 Question: How did they induce the CFG?

A: the parser first learns to parse simple sentences, then proceeds to learn more complex ones. The induction method is iterative, semi-automatic and based on frequent patterns
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Q: One example illustrates that the mistakes the model makes can be associated with changes in the party policy. 
 Question: Do changes in policies of the political actors account for all of the mistakes the model made?

A: Yes
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Q: We found interesting patterns which are learned by our model and help understand these monolingual gains. For example, a recurring pattern is that words in English which are translated to the same word, or to semantically close words, in the target language end up closer together after our transformation. For example, in the case of English-Spanish the following pairs were among the pairs whose similarity increased the most by applying our transformation: cellphone-telephone, movie-film, book-manuscript or rhythm-cadence, which are either translated to the same word in Spanish (i.e., teléfono and película in the first two cases) or are already very close in the Spanish space. More generally, we found that word pairs which move together the most tend to be semantically very similar and belong to the same domain, e.g., car-bicycle, opera-cinema, or snow-ice.

 
 Question: Why does the model improve in monolingual spaces as well? 

A:
because word pair similarity increases if the two words translate to similar parts of the cross-lingual embedding space
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